Principal NLP Scientist

Sourceability; United States; Remote; USD 180,000 - 260,000; Full time

Job summary

Sourceability is seeking a Principal NLP Scientist to lead the design, research, and improvement of NLP and LLM capabilities for production systems. You will own end-to-end model development, evaluation, and integration into real workflows, affecting automation quality and data processing accuracy.

You will collaborate with software, data engineering, and product teams to define experiments, build prototypes, and move successful models into production, using modern NLP techniques and scalable

Qualifications

• PhD in CS, ML, or related field and 8+ years in ML/NLP.
• 5+ years hands-on experience building NLP models for production systems.
• Deep understanding of Transformer-based architectures and RNN/CNN.
• Experience with PyTorch, Hugging Face, and model evaluation.
• Proficient in Python, data tooling, and cloud platforms.

Responsibilities

• Design, research, and improve NLP models for production business workflows.
• Lead evaluation and integration of NLP solutions across platforms.
• Collaborate with engineers, PMs, analysts, and data annotation teams.

Skills

Python

PyTorch

Hugging Face Transformers

NLP

RAG/GraphRAG

Information extraction

Knowledge graphs

SQL

Cloud (Azure/AWS)

English communication

Education

PhD in CS/ML or related field

Tools

ONNX

Pandas

NumPy

SciPy

scikit-learn

Matplotlib

SQL databases

Azure

AWS

Job description

Sourceability® is a global digital distributor of electronic components transforming how modern businesses bring products to market. With innovation, quality and logistics as the backbone of the company, Sourceability’s cutting‑edge products and services expedite the procurement process across a wide range of industries, including communications/cellular, consumer electronics, and auto manufacturing. The Principal NLP Scientist is a senior technical leader responsible for designing, researching, and improving advanced Natural Language Processing and Large Language Model capabilities for production business systems. This role combines applied research, hands‑on model development, technical architecture, and practical product impact. The Principal NLP Scientist will lead the design of NLP solutions for named entity recognition, text classification, text generation, semantic search, information extraction, and other language‑driven automation use cases. This is not only a research role. The focus is to take modern NLP and LLM technologies and make them reliable, measurable, maintainable, and useful inside real production workflows.

Assigned Product Group Product Group | NLP / AI Automation Stream | Software Engineering / AI & Machine Learning Role Type | Principal-level individual contributor / technical leader

The Principal NLP Scientist will work closely with software engineers, data engineers, product managers, analysts, and data annotation teams to define, build, evaluate, and continuously improve NLP models and language‑based automation systems.

Product Group Focus Areas

The NLP product group is responsible for building and improving systems related to:

• Named entity recognition and structured data extraction
• Text classification and categorization
• Text generation and language‑based automation
• Large Language Model evaluation, adaptation, and integration
• Retrieval‑augmented generation and semantic search
• Knowledge graph and GraphRAG‑based approaches for connecting structured business data, unstructured text, and entity relationships in AI assistant workflows
• Data preparation, annotation strategy, and labeling quality
• Model evaluation, monitoring, and production performance
• Applied NLP research and prototype development
• Integration of NLP models into internal business applications

Insight on Your Impact In this role, you will influence how the company uses modern NLP and LLM technologies across internal platforms and operational workflows. You will define technical direction for NLP systems, evaluate new approaches, design experiments, create prototypes, and help move successful models into production. Your work will directly affect automation quality, data processing accuracy, operational efficiency, and the long‑term AI capabilities of the company.

The role requires strong scientific depth, but also practical engineering judgment. The right candidate should be able to read research papers, understand model architecture, design measurable experiments, and also work with engineers to make sure the final solution can run reliably in production.

Your Qualifications, Your Influence

To be successful in this role, you should have:

• PhD in Computer Science, Machine Learning, Artificial Intelligence, Computational Linguistics, Applied Mathematics, Data Science, or a closely related technical field
• 8+ years of professional experience in machine learning, artificial intelligence, or NLP
• 5+ years of hands‑on experience building NLP models for production or near‑production systems
• Deep understanding of modern neural network architectures, including RNN, CNN, Transformer‑based architectures, attention mechanisms, embeddings, fine‑tuning strategies, layers, modules, and loss functions
• Strong practical experience with NLP tasks such as NER, classification, text generation, semantic similarity, information extraction, and document understanding
• Strong experience with Large Language Models, including model evaluation, prompt design, fine‑tuning, retrieval‑augmented generation, and safe production usage
• Practical understanding of RAG, GraphRAG, knowledge graphs, embeddings, and hybrid retrieval approaches for production LLM applications
• Strong hands‑on experience with Python
• Strong experience with PyTorch and Hugging Face Transformers
• Experience with ONNX or other model optimization / model serving formats
• Strong understanding of data preparation, data quality, labeling workflows, annotation guidelines, and model evaluation metrics
• Practical experience with main data analysis and machine learning libraries, including Pandas, NumPy, SciPy, scikit‑learn, and Matplotlib
• Experience working with SQL databases and structured business data
• Experience with cloud platforms such as Microsoft Azure or AWS
• Ability to design experiments, define success metrics, compare model approaches, and explain trade-offs clearly
• Strong written and verbal English communication skills
• Experience working in Agile engineering environments
• Ability to…